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All things Generative Engine Optimization (GEO). A breakdown of all that's happening in the world of AI search.
Hi, and welcome back to The GEO Show.
This is episode four, and let's get
right into the top stories for today.
First, we have Google just created
a user-controlled visibility
signal inside AI search.
Google has launched something
called an embeddable preferred
sources button on August twentieth.
So readers who select the site via this
button can then find it more easily in
top stories, AI overviews, and AI mode.
More than six hundred thousand unique
sources have already been selected.
Publishers can promote the
selection via the selection link,
via email, or through social.
So this is a very interesting development.
There is now a CTA button that
allows any user to select a site as
a preferred sources site, and I think
this is a very good practice to put
on your blogs, starting with that.
And what that will do is, uh, create
a preference for users to start to
see your pages more in citations,
in AI overviews, and AI mode.
So to me, that is kind of a no-brainer.
I think it, it can only help.
Moving along to the next story, a new
AI entrant is attacking the price of
GEO monitoring, and this entrant is
called Lumo, and that's with a double L.
Double L-U-M-O.
They launched August twentieth with
free AI visibility software using
a bring your own API key model.
It tracks mentions, competitors,
citations, prompt responses, query
fan-out, sentiment, and share a voice
across ChatGPT, Gemini, Perplexity,
Copilot, AI mode, and AI overviews.
So- Yet another competitor has joined
this already very crowded category,
and they're offering a free product.
So I think this was a matter of
time before we started to see the
reporting-only layer of this new
tool category, uh, AEO or GEO, uh,
AI visibility tools, let's call them.
Uh, but now there's a free layer,
so this is really becoming more
and more commoditized by the day.
Uh, basic prompt and citation
monitoring, um, effectively is,
is already a commodity game.
And then the, the goal is for the
existing players of the category to, to
go up the stack and to offer something
more, to offer some differentiation.
So this could be content
creation and/or content strategy.
It could have something to do with
citation building, or it could be
deeper analysis, uh, broader analysis
that encompasses SEO along with GEO.
So I think this is a very, very
fast-changing software category,
and it's very interesting to watch.
One of the things that we decided
with GEOForge from the very beginning
was that we would have a competitive
analytics AI visibility tracking
module, which we called SignalForge.
But that the real alpha, the value
add above that would be ContentForge,
which produces content that is
grounded in a proprietary knowledge
base, and also SiteForge, which allows
users to organize citation building
opportunities and take direct action on
them directly inside of the platform.
So differentiation is key in this
now hyper-competitive category
of AI search visibility tools.
Moving on to the next story.
Creator marketing is officially
entering GEO budgets.
Digiday reports that Zoom and
multiple agencies are experimenting
with creator campaigns specifically
to influence AI visibility.
Agencies including Trevant and
Crispin are auditing which creators
and content receive large language
model citations and then using that
data in their creator selection.
So that is pretty fascinating because now
GEO is starting to influence influencer
marketing itself, and influencers
are being selected in part based
on criteria around their citations.
So I imagine a new workflow being
that you would, uh, engage in a
trial with an influencer, and if that
influencer's content, uh, of course,
you wanna look at its distribution, its
reach, its conversions, and its ROI.
But separately now, there are new
judgment criteria around, uh, the
degree of citation surface area that
that influencer's content may have.
So GEO budgets are now overlapping
with influencer marketing, and GEO is
now influencing influencer selection
within influencer marketing.
Next story.
Google AI Overviews are pulling social
content now at meaningful scale.
This is a study from BrightEdge, which
analyzed more than three hundred million
monthly US searches and found Facebook
cited nineteen point five million times
in AI Overviews, Instagram roughly eight
hundred and seventy-seven thousand times,
and TikTok seventy-eight thousand times.
So interestingly, um, Facebook is
overwhelmingly number one, about, uh,
ten x or twenty x larger than Instagram
at number two, which is also ten x
larger than TikTok at number three.
Uh, regardless, this analysis
argues that exact answer relevance
matters more than follower size.
So apparently what they're seeing here
is That long-tail social media posts,
not only from mega-influencers, uh, but
regular long-tail social media posts from
users that don't have huge followings
but that have very, very hyper-relevant
posts are starting to appear now within
AI overviews at some significant scale.
So, uh, what this is suggesting
to me is that social posts are
increasingly part of the retrieval
layer in GEO, not just distribution.
So we've for a while thought about
social media as a way to distribute
repurposed content that initially gets
created and optimized for SEO and GEO.
So an example would be that you have
an optimized blog post that gets
repurposed, let's say, into a video,
and then that video gets published on
YouTube, Facebook, Instagram, TikTok, etc.
And that is a distribution strategy.
But now, as we see more of these social
media assets appearing in AI overviews,
and these are not only YouTube, which
of course dominates, but also now
Facebook, Instagram, and TikTok, then
it becomes also a retrieval layer
in addition to a distribution game.
So what does that mean for video creators?
Uh, LinkedIn video community content,
this should contain concrete facts,
explanations, benchmarks, and
technical answers that can stand
alone when they're retrieved by AI.
So that actually suggests a slightly
different approach in creating those
videos because you think of them as
not only distribution assets, but as
citation sources that need to stand
on their own, let's say, in a, in a s-
list of sources within an LLM response.
All right, moving on to the
next story This is about
listicles, my favorite topic.
Uh, writing your own best tools article
or listicle can actually help your
competitor more than it helps you.
And this is analysis coming from
Search Engine Journal and Ahrefs.
Ahrefs published thirty-four
self-promotional listicles across five
domains, and they tracked nine thousand
eight hundred and eighty-six AI answers.
And what they saw was AI systems
frequently used the list as a source
without recommending its publisher.
And in one experiment that
promotes Ahrefs' own conference, a
competing event was recommended in
forty-three percent of the answers.
So one of the most popular tactics now
for citation building, and especially
within SaaS and software, is the listicle.
You create a best ten XYZ tools
for fill in the blank, and
you put yourself in that list.
Often, you put your own brand
number one in your own listicle.
And these have tended to perform
pretty well and get picked up
well, uh, by LLMs and displayed
well in LLM citation sources.
Um, but what we're seeing is that this
doesn't always show your brand in the
best light because AI can take that same
list and recommend your competitors over
you, uh, using your list as a source, but
then, uh, not actually citing that source.
So you really don't get any
of the va-- the intended value
there that you hoped for.
And I have also seen recently
that listicles as a share of
total citations is now declining.
So apparently, ChatGPT and others
have started to see this as a
quasi-manipulative way to, to just get
more mentions of your brand on the web.
All right, moving on to the next story.
AI visibility looks increasingly
meaningless as a single blended KPI.
A company called Fractal ran ninety-six
prompts fifteen times each across
ChatGPT Four Oh, Gemini Two Point Five
Flash, and Claude Sonnet Four Point Six,
generating four thousand three hundred
and twenty responses and eight thousand
five hundred plus brand references.
What they found was that
only eleven percent of brands
appeared across all three models.
Seventy-seven percent
appeared only in one.
And this is something that I have long
suspected and we're, we're steadily
seeing more and more evidence, which
is that a blended share of voice
across models is really meaningless
when your share of voice differs so
drastically across the different models.
And we now have enough evidence within
GEOforge to see dramatic differences
in your mention rate and your citation
rate within, let's say, ChatGPT versus
Google AI Overviews or Google AI Mode.
What we have seen is that there
is a pretty high correlation of
citations and sources between Google
AI Mode and AI Overviews, but that
we see very little correlation or
similarity in brand visibility across
ChatGPT and Google's AI properties.
And when you add in Claude and
Perplexity and other models,
I think it gets even muddier.
So really the right way and the best
way to report on AI visibility and
share a voice is at the platform
level, and I really do believe this.
You, you have to dig in and find,
uh, the devil in the details rather
than making any concl-- broad
conclusions about a blended KPI
or a blended AI visibility KPI.
All right, moving on to the next story.
European sites may be paying a much
higher AI scraping tax Uh, Tolbit
analyzed AI bots from forty vendors
across three thousand nine hundred and
six publishers, and they found median
scraping on European sites was four
times higher than North American sites,
with one human AI referral per one
hundred and seventy-nine bot visits.
So just let that sink in.
One human AI referral for every
hundred and seventy-nine bot visits.
As European scrape-to-referral ratio
worsened from a hundred and fifty
in Q1 of this year to two hundred
and twenty-seven to one in Q2.
So this ratio, it's
scrape-to-referral ratio.
It means how many pages does an AI bot
scrape relative to how much referral
traffic, human referral traffic that it
delivers to that brand, to that website.
So it, it, it got almost two times worse
in one quarter from a hundred and fifty
to one, meaning a hundred and fifty
scrapes per one AI-referred visit to two
hundred and twenty-seven to one in Q2.
That's just, uh, you know,
phenomenal to see, and I think that
is not gonna reverse anytime soon.
So what does this say?
Uh, one-- for one, that crawler
volume alone is really a terrible GEO
success metric because crawler volume
is far surpassing and growing much
faster than AI-referred human traffic.
So if your primary GEO success metric is
the traffic that you're getting referred
from AI responses, LLM responses, um,
don't look at crawler volume as a proxy
or a predictor for that, uh, because
it's just growing much, much faster than
those referred, those referred visits.
And that's particularly the case for
European sites Uh, that had a four
times higher, uh, crawl-to-visit ratio
than the North American counterparts.
I have no idea why Europe
gets crawled so much more.
Uh, it could have to
do with, with privacy.
Um, that's just, uh, a wild
guess on my part, though.
All right, let's move on to
the last story of the day.
New data reconciles that SEO
still matters, with ranking
number one isn't enough.
So, uh, let's, let's unpack this.
Ahrefs analyzed one point four million
ChatGPT prompts, and they attributed
eighty-eight point four six percent of
the citations to the general search index.
So they took all the citations, and they
wanted to see basically, uh, where they
are ranking for equivalent searches.
Uh, what they found was that across
eight hundred and sixty-three thousand
of the total one point four million
Google result pages and, uhรขยยฆ
Sorry, let me, let me repeat that.
Across eight hundred and sixty-three
thousand Google result pages and
four million AI overview citations,
only about thirty-eight percent of
cited URLs ranked top ten for the
original query, and that is down from
seventy-six percent a year earlier.
So let me, let me try to
explain that in simple terms.
AI overview citations only ranked in
classic search results thirty-eight
percent of the time in the top ten.
And a year ago, they ranked
seventy-six percent of the time
they ranked in the top ten results.
So this is a divergence of
AI overview-- Uh, I'm sorry.
Of, yes, AI overview
citations and search queries.
So that means that it no longer really
matters to rank on the first page of
Google if your goal is to maximize
your citations within AI overviews,
because the query fan-out process
can go much deeper than page one.
Uh, you still definitely need
to get indexed, and you need
to be competitively ranked.
I'd say you just probably should still be
ranked on the first two or three pages.
But being ranked on page one is not the
end-all, be-all, uh, like it used to be in
SEO, because basically only thirty-eight
percent of those citations are ranked
in the top ten for their original query.
So keep those strong SEO fundamentals
because you still need to get indexed
so that the search retrieval process
can find you in-- when it goes out
and does that real-time search.
Um, but also expand your topic
coverage around the evidence questions
that AI systems will fan out into.
All right, that's a wrap for today.
Uh, we got through eight big stories, and
we'll see you next time on episode five.
Thanks for listening.